{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T21:58:10Z","timestamp":1782856690761,"version":"3.54.5"},"reference-count":54,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62076251"],"award-info":[{"award-number":["62076251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371469"],"award-info":[{"award-number":["62371469"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.knosys.2026.116295","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:02:50Z","timestamp":1781827370000},"page":"116295","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A multi-scale adaptive graph convolution approach to multivariate time series forecasting"],"prefix":"10.1016","volume":"349","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6884-2817","authenticated-orcid":false,"given":"Xiao","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8615-7313","authenticated-orcid":false,"given":"Zhisong","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0645-9164","authenticated-orcid":false,"given":"Chengcheng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.knosys.2026.116295_b1","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1016\/j.ijforecast.2019.07.001","article-title":"DeepAR: Probabilistic forecasting with autoregressive recurrent networks","volume":"36","author":"Salinas","year":"2020","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.knosys.2026.116295_b2","doi-asserted-by":"crossref","unstructured":"Z. Yue, Y. Wang, J. Duan, T. Yang, C. Huang, Y. Tong, B. Xu, Ts2vec: Towards universal representation of time series, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 2022, pp. 8980\u20138987.","DOI":"10.1609\/aaai.v36i8.20881"},{"key":"10.1016\/j.knosys.2026.116295_b3","doi-asserted-by":"crossref","unstructured":"H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, Informer: Beyond efficient transformer for long sequence time-series forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35, 2021, pp. 11106\u201311115.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"10.1016\/j.knosys.2026.116295_b4","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"10.1016\/j.knosys.2026.116295_b5","doi-asserted-by":"crossref","unstructured":"Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, C. Zhang, Connecting the dots: Multivariate time series forecasting with graph neural networks, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 753\u2013763.","DOI":"10.1145\/3394486.3403118"},{"key":"10.1016\/j.knosys.2026.116295_b6","series-title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","first-page":"2362","article-title":"Regularized graph structure learning with semantic knowledge for multi-variates time-series forecasting","author":"Yu","year":"2022"},{"key":"10.1016\/j.knosys.2026.116295_b7","doi-asserted-by":"crossref","unstructured":"Z. Shao, Z. Zhang, F. Wang, Y. Xu, Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting, in: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 1567\u20131577.","DOI":"10.1145\/3534678.3539396"},{"key":"10.1016\/j.knosys.2026.116295_b8","doi-asserted-by":"crossref","first-page":"19414","DOI":"10.52202\/068431-1411","article-title":"Multivariate time-series forecasting with temporal polynomial graph neural networks","volume":"35","author":"Liu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.knosys.2026.116295_b9","doi-asserted-by":"crossref","first-page":"753","DOI":"10.14778\/3636218.3636230","article-title":"Multiple time series forecasting with dynamic graph modeling","volume":"17","author":"Zhao","year":"2023","journal-title":"Proc. the VLDB Endow."},{"key":"10.1016\/j.knosys.2026.116295_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109423","article-title":"Dynamic graph structure learning for multivariate time series forecasting","volume":"138","author":"Li","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116295_b11","first-page":"17804","article-title":"Adaptive graph convolutional recurrent network for traffic forecasting","volume":"33","author":"Bai","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116295_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120259","article-title":"DAGCRN: Graph convolutional recurrent network for traffic forecasting with dynamic adjacency matrix","volume":"227","author":"Shi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2026.116295_b13","series-title":"Discrete graph structure learning for forecasting multiple time series","author":"Shang","year":"2021"},{"key":"10.1016\/j.knosys.2026.116295_b14","series-title":"Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting","author":"Chen","year":"2024"},{"issue":"3","key":"10.1016\/j.knosys.2026.116295_b15","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1287\/mnsc.6.3.324","article-title":"Forecasting sales by exponentially weighted moving averages","volume":"6","author":"Winters","year":"1960","journal-title":"Manag. Sci."},{"key":"10.1016\/j.knosys.2026.116295_b16","series-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"issue":"3","key":"10.1016\/j.knosys.2026.116295_b17","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.knosys.2026.116295_b18","doi-asserted-by":"crossref","unstructured":"T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"10.1016\/j.knosys.2026.116295_b19","series-title":"2016 31st Youth Academic Annual Conference of Chinese Association of Automation","first-page":"324","article-title":"Using LSTM and GRU neural network methods for traffic flow prediction","author":"Fu","year":"2016"},{"key":"10.1016\/j.knosys.2026.116295_b20","series-title":"An empirical evaluation of generic convolutional and recurrent networks for sequence modeling","author":"Bai","year":"2018"},{"key":"10.1016\/j.knosys.2026.116295_b21","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116295_b22","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.116295_b23","series-title":"Itransformer: Inverted transformers are effective for time series forecasting","author":"Liu","year":"2023"},{"key":"10.1016\/j.knosys.2026.116295_b24","series-title":"Scaleformer: Iterative multi-scale refining transformers for time series forecasting","author":"Shabani","year":"2022"},{"key":"10.1016\/j.knosys.2026.116295_b25","unstructured":"H. Wang, J. Peng, F. Huang, J. Wang, J. Chen, Y. Xiao, Micn: Multi-scale local and global context modeling for long-term series forecasting, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116295_b26","doi-asserted-by":"crossref","unstructured":"A. Zeng, M. Chen, L. Zhang, Q. Xu, Are transformers effective for time series forecasting?, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37, 2023, pp. 11121\u201311128.","DOI":"10.1609\/aaai.v37i9.26317"},{"key":"10.1016\/j.knosys.2026.116295_b27","series-title":"DEPTS: Deep expansion learning for periodic time series forecasting","author":"Fan","year":"2022"},{"key":"10.1016\/j.knosys.2026.116295_b28","series-title":"Timesnet: Temporal 2d-variation modeling for general time series analysis","author":"Wu","year":"2022"},{"key":"10.1016\/j.knosys.2026.116295_b29","series-title":"A time series is worth 64 words: Long-term forecasting with transformers","author":"Nie","year":"2022"},{"issue":"1971","key":"10.1016\/j.knosys.2026.116295_b30","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"10.1016\/j.knosys.2026.116295_b31","series-title":"IJCAI","first-page":"2285","article-title":"Time-aware multi-scale RNNs for time series modeling","author":"Chen","year":"2021"},{"issue":"10","key":"10.1016\/j.knosys.2026.116295_b32","doi-asserted-by":"crossref","first-page":"10748","DOI":"10.1109\/TKDE.2023.3268199","article-title":"Multi-scale adaptive graph neural network for multivariate time series forecasting","volume":"35","author":"Chen","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116295_b33","series-title":"International Conference on Machine Learning","first-page":"27268","article-title":"Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting","author":"Zhou","year":"2022"},{"key":"10.1016\/j.knosys.2026.116295_b34","unstructured":"S. Liu, H. Yu, C. Liao, J. Li, W. Lin, A.X. Liu, S. Dustdar, Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting, in: International Conference on Learning Representations, 2021."},{"key":"10.1016\/j.knosys.2026.116295_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102607","article-title":"Mgsfformer: A multi-granularity spatiotemporal fusion transformer for air quality prediction","volume":"113","author":"Yu","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.knosys.2026.116295_b36","series-title":"Advances in Neural Information Processing Systems","first-page":"12345","article-title":"Graph transformer for multivariate time series forecasting","volume":"Vol. 36","author":"Example","year":"2024"},{"key":"10.1016\/j.knosys.2026.116295_b37","series-title":"How expressive are spectral-temporal graph neural networks for time series forecasting?","author":"Jin","year":"2023"},{"key":"10.1016\/j.knosys.2026.116295_b38","series-title":"IJCAI","first-page":"2355","article-title":"LSGCN: Long short-term traffic prediction with graph convolutional networks.","volume":"Vol. 7","author":"Huang","year":"2020"},{"key":"10.1016\/j.knosys.2026.116295_b39","series-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting","author":"Yu","year":"2017"},{"key":"10.1016\/j.knosys.2026.116295_b40","series-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting","author":"Li","year":"2017"},{"key":"10.1016\/j.knosys.2026.116295_b41","doi-asserted-by":"crossref","unstructured":"Q. Zhang, J. Chang, G. Meng, S. Xiang, C. Pan, Spatio-temporal graph structure learning for traffic forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 2020, pp. 1177\u20131185.","DOI":"10.1609\/aaai.v34i01.5470"},{"key":"10.1016\/j.knosys.2026.116295_b42","series-title":"Graph wavenet for deep spatial-temporal graph modeling","author":"Wu","year":"2019"},{"issue":"8","key":"10.1016\/j.knosys.2026.116295_b43","doi-asserted-by":"crossref","first-page":"4635","DOI":"10.1109\/TKDE.2025.3569649","article-title":"GinAR+: A robust end-to-end framework for multivariate time series forecasting with missing values","volume":"37","author":"Yu","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.knosys.2026.116295_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103798","article-title":"MetaGNSDformer: Meta-learning enhanced gated non-stationary informer with frequency-aware attention for point-interval remaining useful life prediction of lithium-ion batteries","volume":"69","author":"Cheng","year":"2026","journal-title":"Adv. Eng. Informatics"},{"key":"10.1016\/j.knosys.2026.116295_b45","series-title":"International Conference on Neural Information Processing","first-page":"362","article-title":"Structured sequence modeling with graph convolutional recurrent networks","author":"Seo","year":"2018"},{"key":"10.1016\/j.knosys.2026.116295_b46","article-title":"Parallel multi-scale dynamic graph neural network for multivariate time series forecasting","volume":"149","author":"Liu","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.knosys.2026.116295_b47","article-title":"SDR-GNN: Spectral domain reconstruction graph neural network for incomplete multimodal learning in conversational emotion recognition","volume":"300","author":"Zhang","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116295_b48","unstructured":"T. Kim, J. Kim, Y. Tae, C. Park, J.-H. Choi, J. Choo, Reversible instance normalization for accurate time-series forecasting against distribution shift, in: International Conference on Learning Representations, 2021."},{"key":"10.1016\/j.knosys.2026.116295_b49","doi-asserted-by":"crossref","unstructured":"W. Cai, Y. Liang, X. Liu, J. Feng, Y. Wu, Msgnet: Learning multi-scale inter-series correlations for multivariate time series forecasting, in: Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38, 2024, pp. 11141\u201311149.","DOI":"10.1609\/aaai.v38i10.28991"},{"key":"10.1016\/j.knosys.2026.116295_b50","unstructured":"Y. Zhang, J. Yan, Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.knosys.2026.116295_b51","series-title":"Long-term forecasting with tide: Time-series dense encoder","author":"Das","year":"2023"},{"key":"10.1016\/j.knosys.2026.116295_b52","doi-asserted-by":"crossref","first-page":"5816","DOI":"10.52202\/068431-0421","article-title":"Scinet: Time series modeling and forecasting with sample convolution and interaction","volume":"35","author":"Liu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"10.1016\/j.knosys.2026.116295_b53","doi-asserted-by":"crossref","first-page":"18435","DOI":"10.1109\/JIOT.2024.3363451","article-title":"SageFormer: Series-aware framework for long-term multivariate time-series forecasting","volume":"11","author":"Zhang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.knosys.2026.116295_b54","article-title":"DiM: Improving multivariate time series forecasting with DI embedding and multi-head graph learning mechanism","author":"Mo","year":"2025","journal-title":"Neurocomputing"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512601021X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095070512601021X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T20:50:15Z","timestamp":1782852615000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095070512601021X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":54,"alternative-id":["S095070512601021X"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116295","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multi-scale adaptive graph convolution approach to multivariate time series forecasting","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116295","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116295"}}